






from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
       from transformers import (
           TFBertModel,
           TFCLIPVisionModel,
           TFDeiTModel,
           TFRobertaModel,
           TFVisionTextDualEncoderModel,
           TFViTModel,
           VisionTextDualEncoderConfig,
       )

if is_vision_available():
       from PIL import Image

       from transformers import VisionTextDualEncoderProcessor
def 		lowercase__  (				_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      if isinstance(_lowercase       ,							collections.abc.Iterable):
            return x
      return (x, x)





@require_tf
class 					A__     :

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    str=None   ,						**_SCREAMING_SNAKE_CASE      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  TFVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  model(input_ids=_SCREAMING_SNAKE_CASE   ,						pixel_values=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE )

           self.assertEqual(output['text_embeds'].shape   ,						(input_ids.shape[0], config.projection_dim) )
           self.assertEqual(output['image_embeds'].shape   ,						(pixel_values.shape[0], config.projection_dim) )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=None   ,						**_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  self.get_vision_text_model(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE   ,						text_model=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  model(input_ids=_SCREAMING_SNAKE_CASE   ,						pixel_values=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE )

           self.assertEqual(output['text_embeds'].shape   ,						(input_ids.shape[0], model.config.projection_dim) )
           self.assertEqual(output['image_embeds'].shape   ,						(pixel_values.shape[0], model.config.projection_dim) )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Tuple=None   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  self.get_vision_text_model(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  {'vision_model': vision_model, 'text_model': text_model}
           UpperCamelCase					       =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  model(input_ids=_SCREAMING_SNAKE_CASE   ,						pixel_values=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE )

           self.assertEqual(output['text_embeds'].shape   ,						(input_ids.shape[0], model.config.projection_dim) )
           self.assertEqual(output['image_embeds'].shape   ,						(pixel_values.shape[0], model.config.projection_dim) )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    str=None   ,						**_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  self.get_vision_text_model(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE   ,						text_model=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  model(input_ids=_SCREAMING_SNAKE_CASE   ,						pixel_values=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  output[0].numpy()

           with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  TFVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  model(input_ids=_SCREAMING_SNAKE_CASE   ,						pixel_values=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  after_output[0].numpy()
                 UpperCamelCase					       =  np.amax(np.abs(out_a - out_a ) )
                 self.assertLessEqual(_SCREAMING_SNAKE_CASE   ,						1E-5 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=None   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  self.get_vision_text_model(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE   ,						text_model=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  model(
               input_ids=_SCREAMING_SNAKE_CASE   ,						pixel_values=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE   ,						output_attentions=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  output.vision_model_output.attentions
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						vision_config.num_hidden_layers )

           # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
           UpperCamelCase					       =  to_atuple(vision_model.config.image_size )
           UpperCamelCase					       =  to_atuple(vision_model.config.patch_size )
           UpperCamelCase					       =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
           UpperCamelCase					       =  num_patches + 1
           self.assertEqual(vision_attentions[0].shape[-3:]   ,						(vision_config.num_attention_heads, seq_len, seq_len) )

           UpperCamelCase					       =  output.text_model_output.attentions
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						text_config.num_hidden_layers )

           self.assertEqual(
               text_attentions[0].shape[-3:]   ,						(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  np.abs((a - b) ).max()
           self.assertLessEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						f'Difference between torch and flax is {diff} (>= {tol}).' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_model(**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  self.prepare_config_and_inputs()
           self.check_model_from_pretrained_configs(**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_from_pretrained(**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.prepare_config_and_inputs()
           self.check_save_load(**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  self.prepare_config_and_inputs()
           self.check_vision_text_output_attention(**_SCREAMING_SNAKE_CASE )







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  self.get_pretrained_model_and_inputs()

           UpperCamelCase					       =  model_a(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  outputs[0].numpy()

           with tempfile.TemporaryDirectory() as tmp_dirname:
                 model_a.save_pretrained(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  TFVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  model_a(**_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  after_outputs[0].numpy()
                 UpperCamelCase					       =  np.amax(np.abs(out_a - out_a ) )
                 self.assertLessEqual(_SCREAMING_SNAKE_CASE   ,						1E-5 )





@require_tf
class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               'hf-internal-testing/tiny-random-vit'   ,						'hf-internal-testing/tiny-random-bert' )
           UpperCamelCase					       =  13
           UpperCamelCase					       =  floats_tensor(
               [
                   batch_size,
                   model.vision_model.config.num_channels,
                   model.vision_model.config.image_size,
                   model.vision_model.config.image_size,
               ] )
           UpperCamelCase					       =  ids_tensor([batch_size, 4]   ,						model.text_model.config.vocab_size )
           UpperCamelCase					       =  random_attention_mask([batch_size, 4] )
           UpperCamelCase					       =  {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

           return model, inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  TFViTModel(_SCREAMING_SNAKE_CASE   ,						name='vision_model' )
           UpperCamelCase					       =  TFBertModel(_SCREAMING_SNAKE_CASE   ,						name='text_model' )
           return vision_model, text_model







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  TFViTModelTester(self )
           UpperCamelCase					       =  TFBertModelTester(self )
           UpperCamelCase					       =  vit_model_tester.prepare_config_and_inputs()
           UpperCamelCase					       =  bert_model_tester.prepare_config_and_inputs()

           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  vision_config_and_inputs

           (
               (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  	
           )					       =  text_config_and_inputs

           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": input_mask,
               "input_ids": input_ids,
               "text_token_type_ids": token_type_ids,
               "text_sequence_labels": sequence_labels,
               "text_token_labels": token_labels,
               "text_choice_labels": choice_labels,
           }





@require_tf
class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               'Rocketknight1/tiny-random-deit-tf'   ,						'hf-internal-testing/tiny-random-roberta' )
           UpperCamelCase					       =  13
           UpperCamelCase					       =  floats_tensor(
               [
                   batch_size,
                   model.vision_model.config.num_channels,
                   model.vision_model.config.image_size,
                   model.vision_model.config.image_size,
               ] )
           UpperCamelCase					       =  ids_tensor([batch_size, 4]   ,						model.text_model.config.vocab_size )
           UpperCamelCase					       =  random_attention_mask([batch_size, 4] )
           UpperCamelCase					       =  {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

           return model, inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Tuple=None   ,						**_SCREAMING_SNAKE_CASE      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  self.get_vision_text_model(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE   ,						text_model=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  model(
               input_ids=_SCREAMING_SNAKE_CASE   ,						pixel_values=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE   ,						output_attentions=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  output.vision_model_output.attentions
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						vision_config.num_hidden_layers )

           # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
           UpperCamelCase					       =  to_atuple(vision_model.config.image_size )
           UpperCamelCase					       =  to_atuple(vision_model.config.patch_size )
           UpperCamelCase					       =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
           UpperCamelCase					       =  num_patches + 2
           self.assertEqual(vision_attentions[0].shape[-3:]   ,						(vision_config.num_attention_heads, seq_len, seq_len) )

           UpperCamelCase					       =  output.text_model_output.attentions
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						text_config.num_hidden_layers )

           self.assertEqual(
               text_attentions[0].shape[-3:]   ,						(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  TFDeiTModel(_SCREAMING_SNAKE_CASE   ,						name='vision_model' )
           UpperCamelCase					       =  TFRobertaModel(_SCREAMING_SNAKE_CASE   ,						name='text_model' )
           return vision_model, text_model







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  TFDeiTModelTester(self )
           UpperCamelCase					       =  TFRobertaModelTester(self )
           UpperCamelCase					       =  vit_model_tester.prepare_config_and_inputs()
           UpperCamelCase					       =  bert_model_tester.prepare_config_and_inputs()

           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  vision_config_and_inputs

           (
               (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  	
           )					       =  text_config_and_inputs

           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": input_mask,
               "input_ids": input_ids,
               "text_token_type_ids": token_type_ids,
               "text_sequence_labels": sequence_labels,
               "text_token_labels": token_labels,
               "text_choice_labels": choice_labels,
           }





@require_tf
class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               'Rocketknight1/tiny-random-clip-tf'   ,						'hf-internal-testing/tiny-random-bert' )
           UpperCamelCase					       =  13
           UpperCamelCase					       =  floats_tensor(
               [
                   batch_size,
                   model.vision_model.config.num_channels,
                   model.vision_model.config.image_size,
                   model.vision_model.config.image_size,
               ] )
           UpperCamelCase					       =  ids_tensor([batch_size, 4]   ,						model.text_model.config.vocab_size )
           UpperCamelCase					       =  random_attention_mask([batch_size, 4] )
           UpperCamelCase					       =  {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

           return model, inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  TFCLIPVisionModel(_SCREAMING_SNAKE_CASE   ,						name='vision_model' )
           UpperCamelCase					       =  TFBertModel(_SCREAMING_SNAKE_CASE   ,						name='text_model' )
           return vision_model, text_model







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  TFCLIPVisionModelTester(self )
           UpperCamelCase					       =  TFBertModelTester(self )
           UpperCamelCase					       =  clip_model_tester.prepare_config_and_inputs()
           UpperCamelCase					       =  bert_model_tester.prepare_config_and_inputs()

           UpperCamelCase       ,  UpperCamelCase					       =  vision_config_and_inputs

           (
               (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  (
               UpperCamelCase	
           )       ,  	
           )					       =  text_config_and_inputs

           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": input_mask,
               "input_ids": input_ids,
               "text_token_type_ids": token_type_ids,
               "text_sequence_labels": sequence_labels,
               "text_token_labels": token_labels,
               "text_choice_labels": choice_labels,
           }





@require_vision
@require_tf
class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  TFVisionTextDualEncoderModel.from_pretrained(
               'clip-italian/clip-italian'   ,						logit_scale_init_value=1.0   ,						from_pt=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian' )

           UpperCamelCase					       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
           UpperCamelCase					       =  processor(
               text=['una foto di un gatto', 'una foto di un cane']   ,						images=_SCREAMING_SNAKE_CASE   ,						padding=_SCREAMING_SNAKE_CASE   ,						return_tensors='np' )

           UpperCamelCase					       =  model(**_SCREAMING_SNAKE_CASE )

           # verify the logits
           self.assertEqual(outputs.logits_per_image.shape   ,						(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
           self.assertEqual(
               outputs.logits_per_text.shape   ,						(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])   ,						)

           UpperCamelCase					       =  np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )

           self.assertTrue(np.allclose(outputs.logits_per_image.numpy()   ,						_SCREAMING_SNAKE_CASE   ,						atol=1E-3 ) )







"""simple docstring"""



from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowercase_							=							_symbol_database.Default()


lowercase_							=							_descriptor_pool.Default().AddSerializedFile(
    b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

lowercase_							=							globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
		lowercase_							=							None
		lowercase_							=							b'H\003'
		# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
		# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
		# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
		# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
		# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
		lowercase_							=							45
		lowercase_							=							15_81
		lowercase_							=							15_17
		lowercase_							=							15_70
		lowercase_							=							15_84
		lowercase_							=							17_93
		lowercase_							=							17_95
		lowercase_							=							19_16
		lowercase_							=							18_64
		lowercase_							=							19_05
		lowercase_							=							19_19
		lowercase_							=							24_29
		lowercase_							=							22_08
		lowercase_							=							24_18
		lowercase_							=							23_23
		lowercase_							=							24_07
# @@protoc_insertion_point(module_scope)